Feature Engineering and Selection Flashcards
7 cards from real DSE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Feature Engineering and Selection flashcards as text
What is the 'curse of dimensionality' and how does it affect feature engineering decisions?
Answer: Data becoming increasingly sparse as the number of features grows, degrading model performance
As dimensionality increases, data points become sparse and distance metrics lose meaning, motivating dimensionality reduction and careful feature selection.
Which of the following is an advantage of mean encoding (target encoding) over one-hot encoding for high-cardinality categoricals?
Answer: It reduces the dimensionality increase caused by many unique categories
Mean encoding replaces each category with the mean target value, keeping the feature as a single column instead of creating hundreds of binary columns.
In time-series feature engineering, what is a 'lag feature'?
Answer: A feature that uses a past observation's value as a predictor for the current time step
Lag features use values from previous time steps (e.g., sales yesterday) as predictors for the current time step.
What does the Variance Inflation Factor (VIF) measure in feature selection?
Answer: The degree to which a feature's variance is explained by other features, indicating multicollinearity
VIF quantifies how much a feature's variance is inflated due to correlation with other features; VIF > 10 typically signals severe multicollinearity.
What is 'feature crosses' as used in systems like TensorFlow Feature Columns?
Answer: Synthetic features created by crossing (combining) two or more categorical features
Feature crosses combine categorical features to create new synthetic features that capture interactions, such as crossing 'city' and 'day_of_week'.
Which of the following best describes 'polynomial feature expansion'?
Answer: Generating new features as powers and cross-products of original features up to a specified degree
Polynomial expansion creates new features like x², x³, and x₁·x₂ to allow linear models to fit non-linear relationships.
Why might you use Recursive Feature Elimination (RFE) instead of simply selecting the top-k features by importance score?
Answer: RFE accounts for feature interactions by re-evaluating importances after each removal
RFE iteratively removes the least important features and retrains, so the importance of remaining features is reassessed in the context of those still present.